Paul Bleicher, MD, Chief Executive Officer, OptumLabs™

19 January 2015

Linked EHR/Claims Data in the High Cost/High Needs Population

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OptumLabs™: Investigate. Collaborate. Innovate.

We accelerate research, innovation and translation by giving our partners access to the largest U.S. linked EHR/claims patient database, thought leadership and the power of multi-partner collaboration

Data

EHR/claims linked data asset and more

Expertise

Data analytics, health economics

Convening

Bringing together partners to work together and share results and insights

Thought leadership

Through OptumLabs, its partners, and collaborators

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Our data today: Overview

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20+ years of data captured in over 1500 fields

• Claims: Facility, Physician, Lab and Pharmacy

Claims, Lab results, Mortality information

• Enrollment: Member coverage periods,

Continuous enrollment, Benefit type

• Sociodemographics: Race, income,

education, net worth

• Health Risk Assessment

Claims (linked):

>130 MM people

EHR (linked): >48 MM patients

Expanded insights with deeper clinical context

250+ additional data fields

Consumer

profile

Expanded insights with consumer profile data for over 37 million consumers

Consumer & Lifestyle data fields – Household composition,

Occupation, Lifestyle interests, Property Equity, Net Worth

Claims: >33 MM people

• Clinical Orders

• Clinical Results

• Diagnoses

• Encounters

• Medications

• Procedures

• Vital Signs

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What unique leverage does linked EHR data provide beyond claims data in understanding high need / high cost

patients?

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Comorbidity Profile of Hospitalized CHF Patients (N = 107,762)

Annual Hospitalization

Rate

Nearly as many patients

have 5 comorbid

conditions as have 1

Claims: Population Level Information on Complex Comorbities

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14,000 incident CHF patients

Hospitalized vs. non-hospitalized patients

3-fold difference in costs during months 4 – 10 post diagnosis

Claims: Rapid Population-Based Comparisons Using an OptumLabs Data Tool

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Claims: Identification of Similar Patient Clusters Supports Care Personalization

Obesity and most MCCs

CAD

Dementia

COPD

Diabetes

Renal

Disease

Dementia, Stroke,

Paralysis

Arrhythmia w/out DM

COPD, Pneumonia Diabetes

w/out compl

Fewest MCCs

CKD with and w/out

DM

Obesity

Clustering of

patients at

highest risk for

hospitalization

(top 10%)

Clustering

strategy shifts

focus to patient

profile

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EHR Supplies Enhanced and Unique Elements; - Optum EHR Includes NLP Sourced Variables

Care Area Labs Visits Provider Diagnosis Procedure

NLP

Drug

Rationale

NLP

Signs,

Disease, &

Symptoms

NLP

Family

History

NLP

Measures

Enrollment Health

Service

Costs

Prescriptions

Filled

Medication

Admins

Microbiology Immunizations Observations

Prescriptions

Written

Patient

Reported

Meds

Patient

Reported

Measures

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Hypotheses Related to Care Management and Readmission Drivers Emerge

Day 1 Day 7 Day 14 Day 21

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Mining Data Through Natural Language Processing Offers Detail Not Present in Structured Fields

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Predictive Modeling Reduced Hospital Admissions by 60%

Heart Failure Hospitalization

Predictive Model:

Used patient prior health care

utilization and clinical findings

(pO2, lab results, vitals) to

predict risk

Contacting and assessing high

risk patients reduced HF

admissions by 60% from prior

year

Effects reverberated through

care system

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• Clinical findings not available in claims

– Laboratory values

– Radiology Findings

– Clinical measurements (e.g. spirometry, LVEF)

– Vitals, smoking status, race/ethnicity

– Patient reported outcomes (e.g. Pain, MMSE)

– OTC medications (e.g. aspirin use)

• Pre-adjudicated diagnosis and procedure information

• Detail information during inpatient confinements

– Temporal details

– Medication delivery in hospital

• Clinical notes can be mined for detail not available in structure data

– E.g. Fall Risk

Integrated EHR and Claims Data: Create Unique Value In Managing Complex Populations